This study uses an operations research approach to develop and to test empirically families of quantitative models for predicting length of stay (LOS) in psychiatric hospitals. These models should achieve predictive accuracy through the realistic representation of causal processes, this approach being necessitated by the predictive failure of standard descriptive models for LOS. The research data will be problem-oriented, although diagnostic data will be used if found to be predictive. The statistical analysis will include not only advanced linear models, but will also take advantage of nonmetric approaches, life-table methods, and nonlinear models. Some nonlinear models are likely to be considerably superior to linear models on a priori grounds, with some support from pilot data. The results are expected to be of use in improving utilization review, evaluation, and services planning by providing better tools for analyzing reasons for differences in LOS among different providers of care, and for estimating the impact of changes in the service delivery system. This project will also serve as a prototype of how quantitative causal modeling methods can be applied to the analysis of mental health care service delivery, inpatient or outpatient.